{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "651de65b-8403-44bb-a194-bfdd9018d760",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from vk_download import main\n",
    "#import scipy.sparse as sprs\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "import random\n",
    "import datetime\n",
    "#from threading import Thread\n",
    "#from multiprocessing import Process\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix, lil_matrix, csc_matrix\n",
    "#import scipy.sparse as sprs\n",
    "#from collections import OrderedDict\n",
    "#import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "#import seaborn as sns\n",
    "import networkx as nx\n",
    "from IPython.display import clear_output\n",
    "#from statsmodels.stats.proportion import proportion_confint\n",
    "#import statsmodels.api as sm\n",
    "#from scipy.optimize import minimize\n",
    "import matplotlib as mpl\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3022713-4e68-4e24-82f7-5593bc159d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Support import LoadSeveralExperiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca79d295-b818-4191-9760-3a904436d7b8",
   "metadata": {},
   "source": [
    "# About cognitive constraints ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fcba1056-2023-40e7-bea4-a2017919cf7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#round(np.random.exponential(scale=Arguments['Scale'], size=1)[0]+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "99ae1438-49cd-4671-8213-60672132ea10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1.0 7.0\n",
      "\n",
      "1.0 3.0 32.0\n"
     ]
    }
   ],
   "source": [
    "ScaleValue = 1/2\n",
    "\n",
    "distr1 = np.round(np.random.exponential(scale=ScaleValue, size=20000)) + 1 \n",
    "\n",
    "ScaleValue = 3\n",
    "\n",
    "distr2 = np.round(np.random.exponential(scale=ScaleValue, size=20000)) + 1 \n",
    "\n",
    "print(distr1.min(), np.median(distr1), distr1.max())\n",
    "\n",
    "print('')\n",
    "\n",
    "print(distr2.min(), np.median(distr2), distr2.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "006b6970-5285-40ec-9c39-2029f6ac05a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2602e+04 6.3820e+03 0.0000e+00 8.8200e+02 0.0000e+00 1.1500e+02\n",
      " 1.7000e+01 0.0000e+00 1.0000e+00 1.0000e+00] [1.  1.6 2.2 2.8 3.4 4.  4.6 5.2 5.8 6.4 7. ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "BackGroundColor = 'white'\n",
    "\n",
    "nrows = 1\n",
    "ncols = 2\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(nrows, ncols, 1) \n",
    "#sub.set_title('A. Number of friends $ t_{1} $', fontsize = TitleSize)\n",
    "\n",
    "h = sub.hist(distr1, #density=True, \n",
    "         #bins=21, \n",
    "         color='royalblue', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "print(h[0], h[1])\n",
    "\n",
    "#sub.xaxis.set_ticks(h[1], np.unique(distr1))\n",
    "\n",
    "sub.set_xlabel('number of attended elements', fontsize = AxisSize-5)\n",
    "sub.set_ylabel('frequency', fontsize = AxisSize)\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(nrows, ncols, 2) \n",
    "#sub.set_title('A. Number of friends $ t_{1} $', fontsize = TitleSize)\n",
    "\n",
    "h = sub.hist(distr2, #density=True,\n",
    "         bins=21, \n",
    "         color='darkgreen', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.set_xlabel('number of attended elements', fontsize = AxisSize-5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#sub.set_xscale('log')\n",
    "#sub.set_yscale('log')\n",
    "#sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "#sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "#sub.set_xlabel('number of friends', fontsize = AxisSize)\n",
    "#sub.set_ylabel('count', fontsize = AxisSize)\n",
    "#sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
